Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data

Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data

Abstract Prediction of survival for patients in intensive care units (ICUs) has been subject to intense research. However, no models exist that embrace the multiverse of data in ICUs. It is an open question whether deep learning methods using automated data integration with minimal pre-processing of...

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Bibliographic Details
Main Authors: Hans-Christian Thorsen-Meyer, Davide Placido, Benjamin Skov Kaas-Hansen, Anna P. Nielsen, Theis Lange, Annelaura B. Nielsen, Palle Toft, Jens Schierbeck, Thomas Strøm, Piotr J. Chmura, Marc Heimann, Kirstine Belling, Anders Perner, Søren Brunak
Format: Article
Language:English
Published: Nature Portfolio 2022-09-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-022-00679-6

Internet

https://doi.org/10.1038/s41746-022-00679-6

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